Abstract
Among the deadliest illnesses that is not initially recognized in the context of cancer is brain cancer. Cancerous cells are created when cells proliferate quickly and uncontrollably. The changes in tumour location, size, and shape severely impair the ability to detect brain cancers. The wrong diagnosis of a brain tumour can have terrible and fatal consequences. So there is need of high level accuracy to find the tumor classification. The categorization, segmentation, analysis, and detection of brain cancer are the main topics of this work. In order to aid researchers, This study intends to offer an extensive exploration of the existing literature regarding brain tumor identification through the utilization of magnetic resonance imaging. Three different types of medical imaging for brain cancer were subjected to machine learning approaches (feature extraction, augmentation methods, segmentation, and anatomy of brain tumours) and identifies current issues that must be resolved for various machine learning algorithms to be applied widely in the treatment of personalised brain cancer. Ultimately, this comprehensive review compiles relevant scholarly works concerning brain tumor detection, accentuating its advantages, limitations, progressions, and challenges, thus paving the way for potential forthcoming investigations.
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